A Survey on Deep Learning for Human Activity Recognition

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Fuqiang Gu ◽  
Mu-Huan Chung ◽  
Mark Chignell ◽  
Shahrokh Valaee ◽  
Baoding Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comprehensive Survey ◽  
Conventional Machine

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

2019 ◽  
Vol 6 (5) ◽  
pp. 8553-8562 ◽  
Author(s):  
Valentina Bianchi ◽  
Marco Bassoli ◽  
Gianfranco Lombardo ◽  
Paolo Fornacciari ◽  
Monica Mordonini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Home Environment ◽  
Smart Home ◽  
Integrated Approach ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Smart Home Environment

scholarly journals Virtual Sensors for Optimal Integration of Human Activity Data

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2017 ◽  
Author(s):  
Antonio A. Aguileta ◽  
Ramon F. Brena ◽  
Oscar Mayora ◽  
Erik Molino-Minero-Re ◽  
Luis A. Trejo
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Combination Method ◽  
Learning Methods ◽  
Activity Data ◽  
Virtual Sensors ◽  
Machine Learning Methods ◽  
Source Of Information

Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.

scholarly journals Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

2020 ◽  
Vol 10 (15) ◽  
pp. 5293 ◽  
Author(s):  
Rebeen Ali Hamad ◽  
Longzhi Yang ◽  
Wai Lok Woo ◽  
Bo Wei
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Temporal Models ◽  
Wide Range

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.

scholarly journals A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8227
Author(s):  
Saad Irfan ◽  
Nadeem Anjum ◽  
Nayyer Masood ◽  
Ahmad S. Khattak ◽  
Naeem Ramzan
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Decision Fusion ◽  
Human Activity Recognition ◽  
Transition Activities ◽  
Accuracy And Precision ◽  
Deep Learning Model

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

scholarly journals A Survey of Vision-Based Transfer Learning in Human Activity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2412
Author(s):  
David Ada Adama ◽  
Ahmad Lotfi ◽  
Robert Ranson
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Sensory Information ◽  
Human Activity Recognition ◽  
Future Directions ◽  
Current State ◽  
Comprehensive Survey

Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.

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